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Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

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Showing 3776–3800 of 4856 papers

TitleStatusHype
Computer Vision and Normalizing Flow-Based Defect DetectionCode0
Acoustic Leak Detection in Water Networks—0
Comparison of Anomaly Detectors: Context MattersCode0
Water Level Estimation Using Sentinel-1 Synthetic Aperture Radar Imagery And Digital Elevation Models—0
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework—0
ESAD: End-to-end Deep Semi-supervised Anomaly Detection—0
JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads—0
Removing Class Imbalance using Polarity-GAN: An Uncertainty Sampling Approach—0
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems—0
Modeling asset allocation strategies and a new portfolio performance score—0
No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems—0
Perfect density models cannot guarantee anomaly detection—0
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications—0
Efficient Nonlinear RX Anomaly Detectors—0
Data-Efficient Methods for Dialogue Systems—0
Deep Learning for Medical Anomaly Detection -- A Survey—0
Video Anomaly Detection by Estimating Likelihood of RepresentationsCode0
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network—0
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks—0
Unsupervised Anomaly Detection From Semantic Similarity Scores—0
Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors—0
Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methodsCode0
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection—0
Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road—0
PS-DeVCEM: Pathology-sensitive deep learning model for video capsule endoscopy based on weakly labeled data—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified